Of implementation up and error in this session, we will see how can we implement a simple neuron using
hardware.
So as we can see in this picture.
This is a model of oversimple narrowing where we have inputs from X1, x2 to X and and each input has
a specific rate, not only one W two W and are showing our weight.
All of them are going to air some signal with B, which is forever bias and the bias will say our system.
How is Twiggy's it after that of our next signal is going to activation function.
This is where the magic will happen and finally we can have over output.
So let's take a look at this model here.
We have over inputs.
Let's call them X1 here X two and then two X in.
Each of them has a specific weight.
We are showing the weight with a resistor.
These are our resistors.
W one, W two or R one are two, two RF and which are of our resistors.
And finally we will just feed them to an up EMP.
Here we have over op amp this up AMP will act as our activation function.
So this is our F function and then F of net of course, and then here as a bias, just look at this
model as a bias.
We will just connect it to the battery and then we can just adjust the voltage of the battery to have
a bias that we are looking for.
And finally, this system will give us an output of AC.
You can simply design your neural network using different softwares.
And then if you want to implemented using hardware, you can just use this model.
There are several advantages of using an analogue system compared to the digital system.
One of them would be unadopt systems are much more faster than digital systems, so they can respond
very faster.
But of course there are some limitation for our knowledge of systems.
They are not extensible and we can just use them for a specific purposes.